During November 2025, Stembe focused on optimizing token processing for the Gemini-2.5-flash model in the BerriAI/litellm repository. By leveraging data optimization and model configuration skills, Stembe reduced the cache_read_input_token_cost from 7.5e-08 to 3e-08, directly improving token-reading efficiency and lowering resource consumption. The solution involved targeted adjustments to JSON-based configuration, resulting in faster response times and enhanced scalability for large language model serving. Although the work spanned a single feature over one month, it addressed operational efficiency and cost control, demonstrating a focused approach to performance tuning within the context of high-throughput machine learning pipelines.